In a stunning development that's reshaping the AI landscape, DeepSeek, a Chinese AI startup, has recently made waves with their open-source R1 model. Released in January 2025, DeepSeek R1 has garnered attention not just for matching OpenAI's capabilities at a fraction of the cost (90-95% less), but also for being the first to successfully integrate web search into a reasoning-focused language model.
The Breakthrough
While ChatGPT has long used a similar RAG-based approach for web search with their standard GPT-4o model, they haven't been able to extend this capability to their reasoning-specialized models like OpenAI O1. DeepSeek R1 breaks new ground by being the first to successfully implement web search in a model optimized for complex reasoning tasks, without compromising its analytical capabilities.
Inside the Implementation
DeepSeek's approach is particularly clever because it adapts ChatGPT's proven RAG methodology for reasoning models. Here's how their system processes each query:
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Query Analysis and Keyword Generation: The LLM Search Engine first analyzes the user's input to generate optimized search keywords. For instance, a question about recent AI developments gets broken down into specific search terms like "AI breakthroughs 2025", "artificial intelligence developments 2025". This is often done using a smaller, faster model optimized for keyword generation.
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Index Lookup and URL Selection: Instead of directly using search API results, the system queries a web index to get potential relevant URLs. A smaller model then evaluates these URLs based on their metadata and snippets to determine which ones are most likely to contain relevant information—similar to how a human would scan search results before clicking on the most promising links.
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Real-time Crawling and Content Processing: The system then performs real-time crawling of the selected URLs, much like a human would click and read the most relevant pages. This crawled content goes through several processing steps:
- Content extraction and cleaning
- Relevance scoring
- Snippet optimization
- Metadata enrichment
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Enhanced Prompting: The system combines the processed content from real-time crawls with the original question in a structured format:
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Response Generation: R1 then uses its reasoning capabilities to analyze these results and generate an informed response, showing its work through transparent reasoning traces.
This multi-step approach, combining initial index lookup with selective real-time crawling, mirrors human research behavior more closely than traditional search API index only methods. It allows for more nuanced content selection and deeper understanding of the retrieved information.
Tracking the Impact
The analytics landscape is already adapting to this development. Profound Agent Analytics, which tracks visits from various AI assistants including ChatGPT, OAI Operator, Google, Meta AI, Perplexity, and Apple, has now added support for DeepSeek assistant visits.
Through Profound Agent Analytics, website owners can track these real-time crawling activities, distinguishing between initial index scans and deeper content retrievals. Our platform provides insights into which pages AI systems find most relevant and how they process the content during these real-time crawls.
Industry Response
This breakthrough hasn't gone unnoticed by major players. OpenAI is expected to implement similar techniques for their O1/O3 models in ChatGPT, while Google is rumored to incorporate comparable features with Gemini 2.0 flash-thinking model. The success of DeepSeek's implementation suggests we're seeing the emergence of a new standard in AI capabilities.
Transparency in Reasoning: A Game-Changing Feature
What makes DeepSeek R1's implementation particularly fascinating is its unprecedented transparency in reasoning while incorporating web search. For the first time in a consumer-facing product, users can actually observe how an AI model thinks through problems and makes decisions using real-time web information.
When R1 responds to a query, it doesn't just provide an answer—it shows its entire thought process. Users can see exactly why the model chose certain sources over others, how it evaluates the credibility of information, and how it arrives at its conclusions. This transparency has struck a chord with users and industry leaders alike.
DeepSeek search feels more sticky even after a few queries because seeing the reasoning (even how earnest it is about what it knows and what it might not know) increases user trust by quite a lot
As Garry Tan, CEO of Y Combinator, noted on X: "DeepSeek search feels more sticky even after a few queries because seeing the reasoning (even how earnest it is about what it knows and what it might not know) increases user trust by quite a lot."
This combination of web search with visible reasoning traces creates a new level of user trust and engagement. When users can follow the model's logic—seeing how it handles uncertainties, acknowledges limitations, and builds arguments—they develop a better understanding of and confidence in the AI's responses. It's like having a transparent partner in research rather than a black box that simply spits out answers.
We expect this trend of transparent reasoning to accelerate across the industry. As companies like OpenAI and Google work to integrate web search into their reasoning models, they're likely to follow DeepSeek's lead in exposing the thinking process to users. This shift towards transparency could fundamentally change how we interact with AI systems, making them more trustworthy and educational tools rather than just answer engines.
Monitoring AI Interactions with Profound Agent Analytics
As AI assistants like DeepSeek R1 revolutionize how web content is consumed and processed, understanding these interactions becomes crucial for website owners and content providers. Profound Agent Analytics has established itself as the leading platform for tracking AI system interactions, now including DeepSeek assistant visits alongside existing coverage of ChatGPT, OpenAI Operator, Google, Meta AI, Perplexity, and Apple.
Our platform offers unique insights into how these AI systems interact with web content:
- Comprehensive Bot Detection: Accurately identify and classify visits from various AI assistants and crawlers
- Interaction Analysis: Understand how AI systems process and interpret your content
- Performance Metrics: Track response times and content accessibility for AI visitors
- Content Optimization: Get actionable insights to improve your content's visibility to AI systems
What sets our solution apart is its server-side approach to analytics. Unlike traditional analytics tools that rely on JavaScript execution, our platform analyzes raw server logs to capture the full spectrum of AI interactions. This is particularly crucial as our research shows that many AI crawlers, including DeepSeek, don't execute JavaScript when accessing content.
Looking Forward
As more companies follow DeepSeek's lead in combining web search with transparent reasoning, the ability to track and optimize for AI visitors will become increasingly crucial. With Profound Agent Analyticss, organizations can stay ahead of this trend, ensuring their content is optimized for both human and AI consumption.
Whether you're a content provider, enterprise platform, or technology company, understanding how AI systems interact with your content is no longer optional—it's essential for staying competitive in an AI-first web landscape.
To learn more about how Profound Agent Analytics can help you understand and optimize for AI visitors, including DeepSeek R1 and other leading AI assistants, visit our .
The age of AI-first content discovery is here, and with it comes the need for new tools and approaches to analytics. As we've seen with DeepSeek R1, the future of AI lies in combining powerful reasoning with transparent processes and real-time information access.